Global Plastic Waste
wasteinyears<-readr::read_csv("./Data/global-plastics-production.csv")## Parsed with column specification:
## cols(
## Entity = col_character(),
## Code = col_character(),
## Year = col_double(),
## `Global plastics production (million tonnes) (tonnes)` = col_double()
## )#glimpse(wasteinyears)
PlasticProductionInallYears<-wasteinyears %>%
ggplot(aes(x=Year,y=`Global plastics production (million tonnes) (tonnes)`))+
geom_line(color='red')+
ggtitle("Plastic Production in Million Tonnes")+
labs(x = "Year", y = "Global Plastic Production (million tonnes)")+
theme_classic()### Plastic Produced in all years
library(plotly)
ggplotly(PlasticProductionInallYears)Animation of Plastic Production in all years:
theme_set(theme_bw())
#glimpse(wasteinyears)
PlasticProductionInallYears<-wasteinyears %>%
ggplot(aes(x=Year,y=`Global plastics production (million tonnes) (tonnes)`,group))+
geom_line(color='red')+ggtitle("Plastic Production in Million Tonnes across all Years")
PlasticProductionInallYears +
geom_point() +
transition_reveal(Year) 
#PlasticProductionInallYears + transition_time(Year) +
#labs(title = "Year: {frame_time}") +
# ease_aes("linear")### Plastic waste generation in 2010
plasticwaste_2010<-readr::read_csv("./Data/plastic-waste-generation-total.csv")## Parsed with column specification:
## cols(
## Entity = col_character(),
## Code = col_character(),
## Year = col_double(),
## `Plastic waste generation (tonnes, total) (tonnes per year)` = col_double()
## )#glimpse(plasticwaste_2010)
Plasticwastegeneration2010<-plasticwaste_2010%>%
filter(Entity %in% c("United States","Germany","Egypt","China","India"))%>%
ggplot(aes(x=reorder(Entity,-`Plastic waste generation (tonnes, total) (tonnes per year)`),y=`Plastic waste generation (tonnes, total) (tonnes per year)`,fill=`Plastic waste generation (tonnes, total) (tonnes per year)`))+
geom_bar(position="dodge",stat = "identity")+coord_flip()+ggtitle("Plastic Waste Generation (2010)") +
scale_fill_distiller(palette = "Reds", direction = 1) +
theme_minimal() +
theme(
panel.grid = element_blank(),
# panel.grid.major.y = element_line(color = "white"),
panel.ontop = TRUE
) +
labs(x = "Country", y = "Plastic Waste Generated (Tonnes)")
library(plotly)
ggplotly(Plasticwastegeneration2010)###chloropleth map of Plastic production in 2010 across different countries.
#install.packages("plotly")
library(plotly)
# light grey boundaries
l <- list(color = toRGB("grey"), width = 0.5)
# specify map projection/options
g <- list(
showframe = FALSE,
showcoastlines = FALSE,
projection = list(type = 'Mercator')
)
plot_geo(plasticwaste_2010) %>%
add_trace(
z=~`Plastic waste generation (tonnes, total) (tonnes per year)`, color = ~`Plastic waste generation (tonnes, total) (tonnes per year)` , colors = 'Reds',
locations = ~Code, marker = list(line = l)
) %>%
colorbar(title = 'Plastic waste in Million Tonnes', ticksuffix = '') %>%
layout(
title = 'Plastic waste produced in 2010',
geo = g
)#Animation of Box plot for the Plastic waste generated in 2010
plasticwaste_2010<-readr::read_csv("./Data/plastic-waste-generation-total.csv")## Parsed with column specification:
## cols(
## Entity = col_character(),
## Code = col_character(),
## Year = col_double(),
## `Plastic waste generation (tonnes, total) (tonnes per year)` = col_double()
## )#glimpse(plasticwaste_2010)
Plasticwastegeneration2010<-plasticwaste_2010%>%
filter(Entity %in% c("United States","Germany","Egypt","China","India"))%>%
ggplot(aes(x=reorder(Entity,-`Plastic waste generation (tonnes, total) (tonnes per year)`),y=`Plastic waste generation (tonnes, total) (tonnes per year)`,fill=`Plastic waste generation (tonnes, total) (tonnes per year)`))+
geom_bar(position="dodge",stat = "identity")+coord_flip()+ggtitle("Plastic waste generation, 2010") +
scale_fill_distiller(palette = "Reds", direction = 1) +
theme_minimal() +
theme(
panel.grid = element_blank(),
# panel.grid.major.y = element_line(color = "white"),
panel.ontop = TRUE
) +
labs(x = "Country", y = "Plastic waste generated in Tonnes")
Plasticwastegeneration2010 + transition_states(`Plastic waste generation (tonnes, total) (tonnes per year)`, wrap = FALSE) +
shadow_mark()
Plastic waste generated per person per day in Kg’s
plasticfate<-readr::read_csv("./Data/global-plastic-fate.csv")## Parsed with column specification:
## cols(
## Entity = col_character(),
## Code = col_logical(),
## Year = col_double(),
## `Estimated historic plastic fate (%)` = col_double()
## )plasticfate## # A tibble: 108 x 4
## Entity Code Year `Estimated historic plastic fate (%)`
## <chr> <lgl> <dbl> <dbl>
## 1 Discarded NA 1980 100
## 2 Discarded NA 1981 98.3
## 3 Discarded NA 1982 97.6
## 4 Discarded NA 1983 96.9
## 5 Discarded NA 1984 96.2
## 6 Discarded NA 1985 95.5
## 7 Discarded NA 1986 94.8
## 8 Discarded NA 1987 94.1
## 9 Discarded NA 1988 92.8
## 10 Discarded NA 1989 91.4
## # … with 98 more rows#glimpse(plasticfate)
plasticwaste_2015<-readr::read_csv("./Data/plastic-waste-by-sector.csv")## Parsed with column specification:
## cols(
## Entity = col_character(),
## Code = col_logical(),
## Year = col_double(),
## `Primary plastic waste generation (million tonnes) (tonnes)` = col_double()
## )#plasticwaste_2015
plasticpercapita<-readr::read_csv("./Data/plastic-waste-per-capita.csv")## Parsed with column specification:
## cols(
## Entity = col_character(),
## Code = col_character(),
## Year = col_double(),
## `Per capita plastic waste (kilograms per person per day)` = col_double()
## )#plasticpercapita
#glimpse(plasticpercapita)
Plasticwastegenerationperperson2010<-plasticpercapita%>%
filter(Entity %in% c("United States","United Kingdom","Brazil","China","Canada"
,"Benin","India"))%>%
ggplot(aes(x=reorder(Entity,-`Per capita plastic waste (kilograms per person per day)`),y=`Per capita plastic waste (kilograms per person per day)`,fill=`Per capita plastic waste (kilograms per person per day)`))+
geom_bar(position="dodge",stat = "identity")+coord_flip()+ggtitle("Plastic waste generation per person, 2010") +
scale_fill_distiller(palette = "Reds", direction = 1) +
theme_minimal() +
theme(
panel.grid = element_blank(),
# panel.grid.major.y = element_line(color = "white"),
panel.ontop = TRUE
) +
labs(x = "plastic_waste_per_person_per_day", y = "Country")
Plasticwastegenerationperperson2010
library(plotly)
ggplotly(Plasticwastegenerationperperson2010)plasticfate<-readr::read_csv("./Data/global-plastic-fate.csv")## Parsed with column specification:
## cols(
## Entity = col_character(),
## Code = col_logical(),
## Year = col_double(),
## `Estimated historic plastic fate (%)` = col_double()
## )plasticfate## # A tibble: 108 x 4
## Entity Code Year `Estimated historic plastic fate (%)`
## <chr> <lgl> <dbl> <dbl>
## 1 Discarded NA 1980 100
## 2 Discarded NA 1981 98.3
## 3 Discarded NA 1982 97.6
## 4 Discarded NA 1983 96.9
## 5 Discarded NA 1984 96.2
## 6 Discarded NA 1985 95.5
## 7 Discarded NA 1986 94.8
## 8 Discarded NA 1987 94.1
## 9 Discarded NA 1988 92.8
## 10 Discarded NA 1989 91.4
## # … with 98 more rows#glimpse(plasticfate)
plasticwaste_2015<-readr::read_csv("./Data/plastic-waste-by-sector.csv")## Parsed with column specification:
## cols(
## Entity = col_character(),
## Code = col_logical(),
## Year = col_double(),
## `Primary plastic waste generation (million tonnes) (tonnes)` = col_double()
## )#plasticwaste_2015
plasticpercapita<-readr::read_csv("./Data/plastic-waste-per-capita.csv")## Parsed with column specification:
## cols(
## Entity = col_character(),
## Code = col_character(),
## Year = col_double(),
## `Per capita plastic waste (kilograms per person per day)` = col_double()
## )#plasticpercapita
#glimpse(plasticpercapita)
Plasticwastegenerationperperson2010<-plasticpercapita%>%
filter(Entity %in% c("United States","United Kingdom","Brazil","China","Canada"
,"Benin","India"))%>%
ggplot(aes(x=reorder(Entity,-`Per capita plastic waste (kilograms per person per day)`),y=`Per capita plastic waste (kilograms per person per day)`,fill=`Per capita plastic waste (kilograms per person per day)`))+
geom_bar(position="dodge",stat = "identity")+coord_flip()+ggtitle("Plastic waste generation per person, 2010") +
scale_fill_distiller(palette = "Reds", direction = 1) +
theme_minimal() +
theme(
panel.grid = element_blank(),
# panel.grid.major.y = element_line(color = "white"),
panel.ontop = TRUE
) +
labs(x = "plastic_waste_per_person_per_day", y = "Country")
Plasticwastegenerationperperson2010 + transition_states(Entity, wrap = FALSE) +
shadow_mark()
Chloropleth map of plastic waste generated per person per day in kg
#install.packages("plotly")
library(plotly)
# light grey boundaries
l <- list(color = toRGB("grey"), width = 0.5)
# specify map projection/options
g <- list(
showframe = FALSE,
showcoastlines = FALSE,
projection = list(type = 'Mercator')
)
plot_geo(plasticpercapita) %>%
add_trace(
z=~`Per capita plastic waste (kilograms per person per day)`, color = ~`Per capita plastic waste (kilograms per person per day)` , colors = 'Blues',
locations = ~Code, marker = list(line = l)
) %>%
colorbar(title = 'Plastic waste in Million Tonnes', ticksuffix = 'kg') %>%
layout(
title = 'Plastic Waste that is Generated per person per day in 2010',
geo = g
)inadequateplastic<-readr::read_csv("./Data/inadequately-managed-plastic.csv")## Parsed with column specification:
## cols(
## Entity = col_character(),
## Code = col_character(),
## Year = col_double(),
## `Share of plastic inadequately managed (%)` = col_double()
## )inadequateplastic## # A tibble: 186 x 4
## Entity Code Year `Share of plastic inadequately managed …
## <chr> <chr> <dbl> <dbl>
## 1 Albania ALB 2010 45
## 2 Algeria DZA 2010 58
## 3 Angola AGO 2010 71
## 4 Anguilla AIA 2010 2
## 5 Antigua and Barbuda ATG 2010 6
## 6 Argentina ARG 2010 12
## 7 Aruba ABW 2010 1
## 8 Australia AUS 2010 0
## 9 Bahamas BHS 2010 1
## 10 Bahrain BHR 2010 10
## # … with 176 more rows#install.packages("plotly")
library(plotly)
# light grey boundaries
l <- list(color = toRGB("grey"), width = 0.5)
# specify map projection/options
g <- list(
showframe = FALSE,
showcoastlines = FALSE,
projection = list(type = 'Mercator')
)
plot_geo(inadequateplastic) %>%
add_trace(
z=~`Share of plastic inadequately managed (%)`, color = ~`Share of plastic inadequately managed (%)` , colors = 'Blues',
locations = ~Code, marker = list(line = l)
) %>%
colorbar(title = 'Plastic waste in Million Tonnes', ticksuffix = '%') %>%
layout(
title = 'Share of plastic waste that is inadequately managed, 2010',
geo = g
)# Read in the data
coast_vs_waste <- readr::read_csv("https://raw.githubusercontent.com/rfordatascience/tidytuesday/master/data/2019/2019-05-21/coastal-population-vs-mismanaged-plastic.csv")## Parsed with column specification:
## cols(
## Entity = col_character(),
## Code = col_character(),
## Year = col_double(),
## `Mismanaged plastic waste (tonnes)` = col_double(),
## `Coastal population` = col_double(),
## `Total population (Gapminder)` = col_double()
## )mismanaged_vs_gdp <- readr::read_csv("https://raw.githubusercontent.com/rfordatascience/tidytuesday/master/data/2019/2019-05-21/per-capita-mismanaged-plastic-waste-vs-gdp-per-capita.csv")## Parsed with column specification:
## cols(
## Entity = col_character(),
## Code = col_character(),
## Year = col_double(),
## `Per capita mismanaged plastic waste (kilograms per person per day)` = col_double(),
## `GDP per capita, PPP (constant 2011 international $) (Rate)` = col_double(),
## `Total population (Gapminder)` = col_double()
## )waste_vs_gdp <- readr::read_csv("https://raw.githubusercontent.com/rfordatascience/tidytuesday/master/data/2019/2019-05-21/per-capita-plastic-waste-vs-gdp-per-capita.csv")## Parsed with column specification:
## cols(
## Entity = col_character(),
## Code = col_character(),
## Year = col_double(),
## `Per capita plastic waste (kilograms per person per day)` = col_double(),
## `GDP per capita, PPP (constant 2011 international $) (constant 2011 international $)` = col_double(),
## `Total population (Gapminder)` = col_double()
## )continent <- readr::read_csv("https://datahub.io/JohnSnowLabs/country-and-continent-codes-list/r/country-and-continent-codes-list-csv.csv")## Parsed with column specification:
## cols(
## Continent_Name = col_character(),
## Continent_Code = col_character(),
## Country_Name = col_character(),
## Two_Letter_Country_Code = col_character(),
## Three_Letter_Country_Code = col_character(),
## Country_Number = col_double()
## )# Clean up names
coast_vs_waste <- clean_names(coast_vs_waste)
mismanaged_vs_gdp <- mismanaged_vs_gdp %>%
set_names(c("entity", "code", "year", "mismg_kg_pp_pd", "gdp_per_capita", "total_population_gapminder"))
waste_vs_gdp <- waste_vs_gdp %>%
set_names(c("entity", "code", "year", "waste_kg_pp_pd", "gdp_per_capita", "total_population_gapminder"))
continent <- clean_names(continent)
continent$continent_code <- ifelse(is.na(continent$continent_code), "NA", continent$continent_code)
# Merge dfs
waste_df <- merge(coast_vs_waste, mismanaged_vs_gdp, by = c("code", "year"))
waste_df <- merge(waste_df, waste_vs_gdp, by = c("code", "year"))
waste_df <- merge(waste_df, continent, by.x = "code", by.y = "three_letter_country_code")
waste_df <- select(waste_df, -c(country_name, two_letter_country_code, country_number))
# Exclude countries with no data
no_data <- waste_df %>%
filter(is.na(mismanaged_plastic_waste_tonnes) & is.na(mismg_kg_pp_pd) & is.na(waste_kg_pp_pd) & year == 2010) %>%
select(code, entity)
waste_df <- waste_df %>%
filter(!code %in% no_data$code) %>%
select(-c(entity.x, entity.y, total_population_gapminder.x, total_population_gapminder.y, gdp_per_capita.x)) %>%
rename(gdp_per_capita = gdp_per_capita.y) %>%
mutate(coastal_prop = coastal_population / total_population_gapminder)
waste_df$coastal_prop <- ifelse(waste_df$coastal_prop > 1, 1, waste_df$coastal_prop)
# Filter to only 2010 data
waste_2010 <- waste_df %>%
filter(!is.na(waste_kg_pp_pd) & !is.na(mismanaged_plastic_waste_tonnes) & !is.na(mismg_kg_pp_pd) &
!is.na(gdp_per_capita) & !is.na(total_population_gapminder & waste_kg_pp_pd < 1)) %>%
select(-year)plt_l <- waste_2010 %>%
ggplot(aes(x = coastal_population, y = mismanaged_plastic_waste_tonnes)) +
geom_point(alpha = 0.75, aes(size = total_population_gapminder, color = continent_name)) +
geom_smooth(method = "lm", se = FALSE, color = "grey20", size = 0.75, linetype = "dotted") +
geom_smooth(data = filter(waste_2010, continent_name == "Oceania"), method = "lm",
se = FALSE, color = "grey20", size = 0.75, linetype = "dashed")+
scale_y_log10()+scale_x_log10()
plt_l
plt_gdp <- waste_2010 %>%
ggplot(aes(x = gdp_per_capita, y = mismg_kg_pp_pd)) +
geom_point(alpha = 0.75, aes(size = total_population_gapminder, color = continent_name)) +
# geom_smooth(method = "lm", se = FALSE, color = "grey20", size = 0.75, linetype = "dotted") +
# geom_smooth(data = filter(waste_2010, continent_name == "Oceania"), method = "lm",
# se = FALSE, color = "grey20", size = 0.75, linetype = "dashed")+
scale_x_log10()
plt_gdp
# Chloropleth of gdp_per_capita vs mismanaged plastic waste in tonnes
#install.packages("plotly")
library(plotly)
# light grey boundaries
l <- list(color = toRGB("grey"), width = 0.5)
# specify map projection/options
g <- list(
showframe = FALSE,
showcoastlines = FALSE,
projection = list(type = 'Mercator')
)
plot_geo(waste_2010) %>%
add_trace(
z = ~mismanaged_plastic_waste_tonnes , color = ~mismanaged_plastic_waste_tonnes , colors = 'Blues',
text = ~continent_name, locations = ~code, marker = list(line = l)
) %>%
colorbar(title = 'Mismanaged Plastic waste in Tonnes', tickprefix = '') %>%
layout(
title = '2010 Global Wastage',
geo = g
)# Proportion of coastal pop vs. mismgd plastic
# Graph setup
plt <- waste_2010 %>%
ggplot(aes(x = coastal_prop, y = mismg_kg_pp_pd)) +
geom_point(alpha = 0.75, aes(size = total_population_gapminder, color = continent_name)) +
geom_smooth(method = "lm", se = FALSE, color = "grey20", size = 0.75, linetype = "dotted") +
geom_smooth(data = filter(waste_2010, continent_name == "Oceania"), method = "lm",
se = FALSE, color = "grey20", size = 0.75, linetype = "dashed")
# Style modifications
plt <- plt +
labs(title = "AFFECT OF RESIDENT PROPORTION ON CONTINENT'S MISMANAGED WASTE PER CAPITA",
caption = "Size of dot = total population",
x = "Coastal Population / Total Population", y = "Mismanaged Waste per Person per Day (kg)") +
theme(text = element_text(family = "Franklin Gothic Medium"),
plot.title = element_text(family = "Franklin Gothic Heavy"),
plot.subtitle = element_text(size = 10),
plot.background = element_rect(fill = "whitesmoke"),
panel.background = element_rect(fill = "whitesmoke"),
legend.background = element_rect(fill = "whitesmoke"),
panel.grid = element_line(color = "grey90")) +
scale_size(guide = "none") +
scale_color_brewer(name = "Continent", palette = "Dark2")
#plot(plt)#install.packages("plotly")
library(plotly)
ggplotly(plt)# Box plot of continent vs. mismanaged waste
oceania_med <- median(filter(waste_2010, continent_name == "Oceania")$mismg_kg_pp_pd)
# Graph setup
plt1 <- waste_2010 %>%
ggplot(aes(x = reorder(continent_name, mismg_kg_pp_pd, FUN = median),y = mismg_kg_pp_pd)) +
geom_boxplot(aes(fill = continent_name))
plt1
# Style modifications
library(RColorBrewer)
plt1 <- plt1 +
scale_fill_manual(values = brewer.pal(6, "Dark2"))+
labs(title = "Continent vs Mismanaged Plastic Waste (PPPD)",
caption = "",
x = "Continent", y = "Mismanaged Waste per Person per Day (kg)") +
theme(text = element_text(family = "Franklin Gothic Medium"),
plot.title = element_text(family = "Franklin Gothic Heavy"),
legend.position = "none",
plot.background = element_rect(fill = "whitesmoke"),
panel.background = element_rect(fill = "whitesmoke"),
panel.grid = element_line(color = "grey90")) +
scale_fill_brewer(palette = "Dark2") +
theme_minimal()## Scale for 'fill' is already present. Adding another scale for 'fill',
## which will replace the existing scale.# Adding annotation
plt1 <- plt1 +
geom_hline(yintercept = oceania_med, linetype = "dashed")
#plot(plt1)
library(plotly)
ggplotly(plt1)#violin
plt1_violin <- waste_2010 %>%
ggplot(aes(x = reorder(continent_name, mismg_kg_pp_pd, FUN = median),y = mismg_kg_pp_pd)) +
geom_violin(aes(fill = continent_name),trim=FALSE) +
labs(title = "Continent vs Mismanaged Plastic Waste (PPPD)",
caption = "",
x = "Continent", y = "Mismanaged Waste per Person per Day (kg)") +
theme(text = element_text(family = "Franklin Gothic Medium"),
plot.title = element_text(family = "Franklin Gothic Heavy"),
legend.position = "none",
plot.background = element_rect(fill = "whitesmoke"),
panel.background = element_rect(fill = "whitesmoke"),
panel.grid = element_line(color = "grey90")) +
scale_fill_brewer(palette = "Dark2")+
theme_minimal()
plt1_violin
### Strip Chart of continent vs. mismanaged waste
plt_strip <- waste_2010 %>%
ggplot(aes(x = reorder(continent_name, mismg_kg_pp_pd, FUN = median),y = mismg_kg_pp_pd)) +
geom_jitter(position=position_jitter(0.2), cex=1.2) +
stat_summary(fun.y=mean, geom="point", shape=18,
size=3, color="red")+
labs(title = "Continent vs Mismanaged Plastic Waste (PPPD)",
caption = "",
x = "Continent", y = "Mismanaged Waste per Person per Day (kg)") +
theme(text = element_text(family = "Franklin Gothic Medium"),
plot.title = element_text(family = "Franklin Gothic Heavy"),
legend.position = "none",
plot.background = element_rect(fill = "whitesmoke"),
panel.background = element_rect(fill = "whitesmoke"),
panel.grid = element_line(color = "grey90")) +
scale_fill_brewer(palette = "Dark2")+
theme_minimal()
plt_strip
# Strip Chart with Violin
plt_strip_violin <- waste_2010 %>%
ggplot(aes(x = reorder(continent_name, mismg_kg_pp_pd, FUN = median),y = mismg_kg_pp_pd)) +
geom_violin(trim = FALSE)+
geom_jitter(position=position_jitter(0.2), cex=1.2) +
stat_summary(fun.y=mean, geom="point", shape=18,
size=3, color="red")+
labs(title = "Continent vs Mismanaged Plastic Waste (PPPD)",
caption = "",
x = "Continent", y = "Mismanaged Waste per Person per Day (kg)") +
theme(text = element_text(family = "Franklin Gothic Medium"),
plot.title = element_text(family = "Franklin Gothic Heavy"),
legend.position = "none",
plot.background = element_rect(fill = "whitesmoke"),
panel.background = element_rect(fill = "whitesmoke"),
panel.grid = element_line(color = "grey90")) +
scale_fill_brewer(palette = "Dark2")+
theme_minimal()
#theme_classic()
plt_strip_violin
#sina plot
#install.packages("ggforce")
library(tidyverse)
library(ggforce)
plt_sina <- waste_2010 %>%
ggplot(aes(x = reorder(continent_name, mismg_kg_pp_pd, FUN = median),y = mismg_kg_pp_pd)) +
geom_sina(aes(colour = continent_name), size = 1.5) +
labs(title = "Continent vs Mismanaged Plastic Waste (PPPD)",
x = "Continent", y = "Mismanaged Waste per Person per Day (kg)") +
theme(text = element_text(family = "Franklin Gothic Medium"),
plot.title = element_text(family = "Franklin Gothic Heavy"),
legend.position = "none",
plot.background = element_rect(fill = "whitesmoke"),
panel.background = element_rect(fill = "whitesmoke"),
panel.grid = element_line(color = "grey90"))+
theme_minimal()
plt_sina
### Box plot of continent vs. coastal proportion
# Box plot of continent vs. coastal proportion
oceania_prop_med <- median(filter(waste_2010, continent_name == "Oceania")$coastal_prop)
# Graph setup
plt2 <- waste_2010 %>%
ggplot(aes(x = reorder(continent_name, coastal_prop, FUN = median),y = coastal_prop)) +
geom_boxplot(aes(fill = continent_name))
# Style modifications
plt2 <- plt2 +
labs(title = "Coastal Population Proportion",
caption = "Source: https://ourworldindata.org/plastic-pollution",
x = "Continent", y = "Coastal Population / Total Population") +
theme(text = element_text(family = "Franklin Gothic Medium"),
plot.title = element_text(family = "Franklin Gothic Heavy"),
legend.position = "none",
plot.background = element_rect(fill = "whitesmoke"),
panel.background = element_rect(fill = "whitesmoke"),
panel.grid = element_line(color = "grey90")) +
scale_fill_brewer(palette = "Dark2")+
theme_minimal()
# Adding annotation
plt2 <- plt2 +
geom_hline(yintercept = oceania_prop_med, linetype = "dashed")
#plot_fin <- grid.arrange(plt, plt1, plt2, layout_matrix = rbind(c(1, 1), c(2, 3)))
#plot(plt2)
#ggsave("MismanagedWastePerCap_CoastalPopProp.jpeg", plt, width = 11.5, height = 8, units = "in", dpi = 320)
library(plotly)
ggplotly(plt2)### Strip Chart of continent vs coastal proportion
plt1_strip <- ggplot(waste_2010, aes(x = reorder(continent_name, coastal_prop, FUN = median),y = coastal_prop)) +
scale_color_manual(values=c("#E69F00", "#56B4E9", "#009E73", "#F0E442", "#0072B2", "#999999")) +
geom_violin(aes(fill = continent_name),trim=FALSE) +
stat_summary(fun.y=mean, geom="point", shape=18,
size=3, color="red")+
labs(title = "continent vs coastal proportion",
caption = "",
x = "Continent", y = "Coastal Proportion") +
theme(text = element_text(family = "Franklin Gothic Medium"),
plot.title = element_text(family = "Franklin Gothic Heavy"),
legend.position = "none",
plot.background = element_rect(fill = "whitesmoke"),
panel.background = element_rect(fill = "whitesmoke"),
panel.grid = element_line(color = "grey90")) +
scale_fill_brewer(palette = "Dark2")+
theme_minimal()
plt1_strip
### Strip Chart of continent vs coastal proportion
plt1_strip <- ggplot(waste_2010, aes(x = reorder(continent_name, coastal_prop, FUN = median),y = coastal_prop)) +
scale_color_manual(values=c("#E69F00", "#56B4E9", "#009E73", "#F0E442", "#0072B2", "#999999")) +
geom_jitter(position=position_jitter(0.2), cex=1.2) +
stat_summary(fun.y=mean, geom="point", shape=18,
size=3, color="red")+
labs(title = "continent vs coastal proportion",
caption = "",
x = "Continent", y = "Coastal Proportion") +
theme(text = element_text(family = "Franklin Gothic Medium"),
plot.title = element_text(family = "Franklin Gothic Heavy"),
legend.position = "none",
plot.background = element_rect(fill = "whitesmoke"),
panel.background = element_rect(fill = "whitesmoke"),
panel.grid = element_line(color = "grey90")) +
scale_fill_brewer(palette = "Dark2")+
theme_minimal()
plt1_strip
### Strip Chart of continent vs coastal proportion
# Strip Chart with Violin
plt1_strip_violin <- ggplot(waste_2010, aes(x = reorder(continent_name, coastal_prop, FUN = median),y = coastal_prop)) +
scale_color_manual(values=c("#E69F00", "#56B4E9", "#009E73", "#F0E442", "#0072B2", "#999999")) +
geom_violin(trim = FALSE)+
geom_jitter(position=position_jitter(0.2), cex=1.2) +
stat_summary(fun.y=mean, geom="point", shape=18,
size=3, color="red")+
labs(title = "continent vs coastal proportion",
caption = "",
x = "Continent", y = "Coastal Proportion") +
theme(text = element_text(family = "Franklin Gothic Medium"),
plot.title = element_text(family = "Franklin Gothic Heavy"),
legend.position = "none",
plot.background = element_rect(fill = "whitesmoke"),
panel.background = element_rect(fill = "whitesmoke"),
panel.grid = element_line(color = "grey90")) +
scale_fill_brewer(palette = "Dark2")+
theme_minimal()
plt1_strip_violin